AI-Based Resume Ranking System Using Semantic Similarity and Skill-Based Scoring

Abstract

The manual screening of vast volumes of resumes presents significant challenges to human resource departments in the contemporary job market, leading to inefficiencies, increased time-to-hire, and potential human bias in candidate selection. This project proposes the development of an AI-powered Resume Ranking System designed to automate and optimize the initial candidate evaluation phase. The system will leverage Natural Language Processing (NLP) techniques and machine learning models to parse candidate resumes and job descriptions. Key features, including skills, experience, education, and keywords, will be extracted and vectorized using advanced embedding models. These vectorized representations will then be utilized in a sophisticated matching algorithm to compute a compatibility score against specific job requirements. The primary objective is to significantly reduce recruiter effort and time, enhance the objectivity of initial screening by mitigating human biases, and improve the overall quality of candidate shortlists. By providing a data-driven ranked list of applicants, this system aims to streamline recruitment workflows, empowering HR professionals to focus on qualitative assessments and candidate engagement, thereby fostering more efficient and equitable hiring decisions.